Overview

Dataset statistics

Number of variables11
Number of observations33898
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory96.0 B

Variable types

DateTime1
Numeric9
Categorical1

Alerts

channel_avg_n_comments_7D has 727 (2.1%) zerosZeros

Reproduction

Analysis started2023-04-07 13:12:58.190238
Analysis finished2023-04-07 13:13:05.747932
Duration7.56 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Distinct5993
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size529.7 KiB
Minimum2022-10-09 13:14:18+00:00
Maximum2023-03-31 04:00:14+00:00
2023-04-07T15:13:05.801051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:05.891428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

view_count
Real number (ℝ)

Distinct33590
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1751796.5
Minimum26482
Maximum1.5074426 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:05.992207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum26482
5-th percentile105618.85
Q1320921
median703349
Q31524981.8
95-th percentile5619751.6
Maximum1.5074426 × 108
Range1.5071778 × 108
Interquartile range (IQR)1204060.8

Descriptive statistics

Standard deviation4761838.9
Coefficient of variation (CV)2.7182603
Kurtosis188.69825
Mean1751796.5
Median Absolute Deviation (MAD)470497.5
Skewness11.145621
Sum5.9382398 × 1010
Variance2.2675109 × 1013
MonotonicityNot monotonic
2023-04-07T15:13:06.081005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
305217 2
 
< 0.1%
266618 2
 
< 0.1%
161343 2
 
< 0.1%
686953 2
 
< 0.1%
958233 2
 
< 0.1%
443308 2
 
< 0.1%
278339 2
 
< 0.1%
208891 2
 
< 0.1%
215651 2
 
< 0.1%
300409 2
 
< 0.1%
Other values (33580) 33878
99.9%
ValueCountFrequency (%)
26482 1
< 0.1%
26986 1
< 0.1%
27724 1
< 0.1%
28094 1
< 0.1%
28694 1
< 0.1%
28703 1
< 0.1%
29097 1
< 0.1%
29318 1
< 0.1%
29397 1
< 0.1%
29801 1
< 0.1%
ValueCountFrequency (%)
150744261 1
< 0.1%
142595834 1
< 0.1%
133488916 1
< 0.1%
123417009 1
< 0.1%
120081662 1
< 0.1%
110562954 1
< 0.1%
109615153 1
< 0.1%
99388127 1
< 0.1%
89148381 1
< 0.1%
86522175 1
< 0.1%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size529.7 KiB
Entertainment
7198 
Sports
6321 
Gaming
6187 
Music
4110 
People & Blogs
2988 
Other values (9)
7094 

Length

Max length20
Median length16
Mean length9.5674376
Min length5

Characters and Unicode

Total characters324317
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowSports
3rd rowSports
4th rowSports
5th rowSports

Common Values

ValueCountFrequency (%)
Entertainment 7198
21.2%
Sports 6321
18.6%
Gaming 6187
18.3%
Music 4110
12.1%
People & Blogs 2988
8.8%
Comedy 1494
 
4.4%
Autos & Vehicles 1030
 
3.0%
Science & Technology 944
 
2.8%
Film & Animation 895
 
2.6%
Education 830
 
2.4%
Other values (4) 1901
 
5.6%

Length

2023-04-07T15:13:06.170606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7758
15.7%
entertainment 7198
14.6%
sports 6321
12.8%
gaming 6187
12.5%
music 4110
8.3%
people 2988
 
6.0%
blogs 2988
 
6.0%
comedy 1494
 
3.0%
autos 1030
 
2.1%
vehicles 1030
 
2.1%
Other values (13) 8310
16.8%

Most occurring characters

ValueCountFrequency (%)
t 33163
 
10.2%
n 32822
 
10.1%
e 29060
 
9.0%
i 24668
 
7.6%
o 20394
 
6.3%
s 17696
 
5.5%
m 16801
 
5.2%
a 15643
 
4.8%
15516
 
4.8%
r 13920
 
4.3%
Other values (24) 104634
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 259387
80.0%
Uppercase Letter 41656
 
12.8%
Space Separator 15516
 
4.8%
Other Punctuation 7758
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 33163
12.8%
n 32822
12.7%
e 29060
11.2%
i 24668
9.5%
o 20394
7.9%
s 17696
 
6.8%
m 16801
 
6.5%
a 15643
 
6.0%
r 13920
 
5.4%
l 10746
 
4.1%
Other values (9) 44474
17.1%
Uppercase Letter
ValueCountFrequency (%)
E 8429
20.2%
S 7857
18.9%
G 6187
14.9%
M 4110
9.9%
P 3896
9.4%
B 2988
 
7.2%
A 2057
 
4.9%
C 1494
 
3.6%
T 1345
 
3.2%
V 1030
 
2.5%
Other values (3) 2263
 
5.4%
Space Separator
ValueCountFrequency (%)
15516
100.0%
Other Punctuation
ValueCountFrequency (%)
& 7758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301043
92.8%
Common 23274
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 33163
 
11.0%
n 32822
 
10.9%
e 29060
 
9.7%
i 24668
 
8.2%
o 20394
 
6.8%
s 17696
 
5.9%
m 16801
 
5.6%
a 15643
 
5.2%
r 13920
 
4.6%
l 10746
 
3.6%
Other values (22) 86130
28.6%
Common
ValueCountFrequency (%)
15516
66.7%
& 7758
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 33163
 
10.2%
n 32822
 
10.1%
e 29060
 
9.0%
i 24668
 
7.6%
o 20394
 
6.3%
s 17696
 
5.5%
m 16801
 
5.2%
a 15643
 
4.8%
15516
 
4.8%
r 13920
 
4.3%
Other values (24) 104634
32.3%
Distinct356
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.64045
Minimum5
Maximum504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:06.256831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile28
Q1111
median218
Q3288
95-th percentile348
Maximum504
Range499
Interquartile range (IQR)177

Descriptive statistics

Standard deviation108.75667
Coefficient of variation (CV)0.53669775
Kurtosis-0.80350732
Mean202.64045
Median Absolute Deviation (MAD)80
Skewness-0.11674056
Sum6869106
Variance11828.014
MonotonicityNot monotonic
2023-04-07T15:13:06.346550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317 640
 
1.9%
286 347
 
1.0%
283 347
 
1.0%
288 282
 
0.8%
306 275
 
0.8%
301 271
 
0.8%
311 270
 
0.8%
293 260
 
0.8%
274 244
 
0.7%
284 242
 
0.7%
Other values (346) 30720
90.6%
ValueCountFrequency (%)
5 50
0.1%
6 49
0.1%
7 44
0.1%
8 8
 
< 0.1%
9 11
 
< 0.1%
10 20
 
0.1%
11 72
0.2%
12 67
0.2%
13 50
0.1%
14 32
0.1%
ValueCountFrequency (%)
504 83
0.2%
499 80
0.2%
466 80
0.2%
462 31
 
0.1%
455 138
0.4%
445 90
0.3%
440 19
 
0.1%
433 39
 
0.1%
429 30
 
0.1%
415 63
0.2%

category_avg_n_views_7D
Real number (ℝ)

Distinct1614
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1758975.7
Minimum74799.833
Maximum11352859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:06.437571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum74799.833
5-th percentile589193.04
Q1957877.76
median1299270.3
Q32163793.7
95-th percentile4381006.5
Maximum11352859
Range11278059
Interquartile range (IQR)1205916

Descriptive statistics

Standard deviation1280774.5
Coefficient of variation (CV)0.72813656
Kurtosis6.5090711
Mean1758975.7
Median Absolute Deviation (MAD)470294.11
Skewness2.1746577
Sum5.962576 × 1010
Variance1.6403834 × 1012
MonotonicityNot monotonic
2023-04-07T15:13:06.524217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1538493.6 138
 
0.4%
1539779.409 114
 
0.3%
1028684.053 112
 
0.3%
3734142.609 110
 
0.3%
1802503.588 104
 
0.3%
1155485.783 99
 
0.3%
2369940.454 97
 
0.3%
1483127.195 95
 
0.3%
2794762.103 93
 
0.3%
864553.218 93
 
0.3%
Other values (1604) 32843
96.9%
ValueCountFrequency (%)
74799.83333 6
 
< 0.1%
87619.25 12
< 0.1%
104481.4286 7
 
< 0.1%
111687.2353 5
 
< 0.1%
129628.8824 12
< 0.1%
137080.3333 5
 
< 0.1%
146182.9167 5
 
< 0.1%
163911 10
< 0.1%
164576.7368 19
0.1%
195111.2973 5
 
< 0.1%
ValueCountFrequency (%)
11352859.15 13
 
< 0.1%
9164009.158 7
 
< 0.1%
8979632.617 9
 
< 0.1%
8722608.378 60
0.2%
8606437.75 15
 
< 0.1%
8162605.857 28
0.1%
8149681.604 22
 
0.1%
8074313.73 20
 
0.1%
8060189.117 5
 
< 0.1%
8001217.296 6
 
< 0.1%
Distinct1614
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4952.6759
Minimum54.583333
Maximum35276.114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:06.612877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum54.583333
5-th percentile1218.9111
Q12306.2552
median3463.1033
Q35524.4966
95-th percentile14995.633
Maximum35276.114
Range35221.531
Interquartile range (IQR)3218.2414

Descriptive statistics

Standard deviation4701.1316
Coefficient of variation (CV)0.94921044
Kurtosis8.7775974
Mean4952.6759
Median Absolute Deviation (MAD)1451.6986
Skewness2.7597684
Sum1.6788581 × 108
Variance22100639
MonotonicityNot monotonic
2023-04-07T15:13:06.698691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4184.098901 138
 
0.4%
4608.636888 114
 
0.3%
1897.099071 112
 
0.3%
27244.28151 110
 
0.3%
4836.522876 104
 
0.3%
2082.101648 99
 
0.3%
6048.660305 97
 
0.3%
6356.809339 95
 
0.3%
12622.27273 93
 
0.3%
1827.017442 93
 
0.3%
Other values (1604) 32843
96.9%
ValueCountFrequency (%)
54.58333333 12
 
< 0.1%
88.29411765 5
 
< 0.1%
333.8 10
 
< 0.1%
394.2666667 5
 
< 0.1%
408.5 6
 
< 0.1%
418.4782609 6
 
< 0.1%
445.2727273 6
 
< 0.1%
537.2857143 7
 
< 0.1%
588.8684211 21
0.1%
592.862963 48
0.1%
ValueCountFrequency (%)
35276.11429 1
 
< 0.1%
31625.04082 3
 
< 0.1%
31077.675 9
 
< 0.1%
30601.07389 6
 
< 0.1%
30432.98058 25
0.1%
30085.6789 35
0.1%
28917.18812 22
0.1%
28350.33835 28
0.1%
28074.18627 30
0.1%
28044.48523 12
 
< 0.1%
Distinct1614
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.202262
Minimum8.8122127
Maximum414.90519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:06.792538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.8122127
5-th percentile14.101034
Q118.734631
median22.350293
Q331.96496
95-th percentile55.536422
Maximum414.90519
Range406.09297
Interquartile range (IQR)13.230329

Descriptive statistics

Standard deviation18.16419
Coefficient of variation (CV)0.64406854
Kurtosis116.8715
Mean28.202262
Median Absolute Deviation (MAD)4.6731434
Skewness7.0359266
Sum956000.27
Variance329.93778
MonotonicityNot monotonic
2023-04-07T15:13:06.880485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.41514143 138
 
0.4%
22.35029343 114
 
0.3%
52.44197338 112
 
0.3%
12.79063599 110
 
0.3%
22.89348893 104
 
0.3%
49.59471696 99
 
0.3%
24.21173313 97
 
0.3%
17.67715003 95
 
0.3%
19.16403999 93
 
0.3%
50.33720592 93
 
0.3%
Other values (1604) 32843
96.9%
ValueCountFrequency (%)
8.812212714 7
 
< 0.1%
8.962186619 28
0.1%
9.203613064 34
0.1%
9.734069319 6
 
< 0.1%
9.764872605 15
< 0.1%
9.772804038 14
< 0.1%
9.817020806 10
 
< 0.1%
10.09641748 1
 
< 0.1%
10.1396995 7
 
< 0.1%
10.14014852 3
 
< 0.1%
ValueCountFrequency (%)
414.9051876 13
< 0.1%
395.6723865 6
< 0.1%
247.9343087 2
 
< 0.1%
218.2694187 12
< 0.1%
153.3788298 10
< 0.1%
151.8580327 6
< 0.1%
149.7723671 4
 
< 0.1%
149.4290223 8
< 0.1%
147.0681675 5
 
< 0.1%
137.3814237 9
< 0.1%
Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7085374
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:06.966200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q38
95-th percentile17
Maximum34
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.1980232
Coefficient of variation (CV)0.544594
Kurtosis7.2408343
Mean7.7085374
Median Absolute Deviation (MAD)1
Skewness2.4502119
Sum261304
Variance17.623399
MonotonicityNot monotonic
2023-04-07T15:13:07.039034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6 9144
27.0%
5 7148
21.1%
7 5234
15.4%
4 1947
 
5.7%
8 1878
 
5.5%
11 1315
 
3.9%
10 1275
 
3.8%
9 1083
 
3.2%
12 888
 
2.6%
14 542
 
1.6%
Other values (23) 3444
 
10.2%
ValueCountFrequency (%)
1 59
 
0.2%
2 86
 
0.3%
3 354
 
1.0%
4 1947
 
5.7%
5 7148
21.1%
6 9144
27.0%
7 5234
15.4%
8 1878
 
5.5%
9 1083
 
3.2%
10 1275
 
3.8%
ValueCountFrequency (%)
34 7
 
< 0.1%
33 12
 
< 0.1%
32 16
 
< 0.1%
30 59
0.2%
29 36
 
0.1%
28 38
 
0.1%
27 97
0.3%
26 91
0.3%
25 76
0.2%
24 55
0.2%

channel_avg_n_views_7D
Real number (ℝ)

Distinct5770
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1727278.7
Minimum29789.4
Maximum1.0492049 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:07.128901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum29789.4
5-th percentile110805.78
Q1336332.45
median736641.17
Q31515929.1
95-th percentile5622355.1
Maximum1.0492049 × 108
Range1.048907 × 108
Interquartile range (IQR)1179596.6

Descriptive statistics

Standard deviation4389452
Coefficient of variation (CV)2.5412528
Kurtosis156.95502
Mean1727278.7
Median Absolute Deviation (MAD)482864.47
Skewness10.371094
Sum5.8551294 × 1010
Variance1.9267289 × 1013
MonotonicityNot monotonic
2023-04-07T15:13:07.218462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
911073.0333 30
 
0.1%
1989694.619 21
 
0.1%
2445473.81 21
 
0.1%
1056070.19 21
 
0.1%
893216.5926 20
 
0.1%
2993020.35 20
 
0.1%
947033.7368 19
 
0.1%
286113.3684 19
 
0.1%
3911324.111 18
 
0.1%
478463.3846 18
 
0.1%
Other values (5760) 33691
99.4%
ValueCountFrequency (%)
29789.4 5
< 0.1%
30716.6 5
< 0.1%
31261.77778 4
< 0.1%
33395 5
< 0.1%
36857.5 6
< 0.1%
38580.25 4
< 0.1%
39489.25 4
< 0.1%
41025 3
< 0.1%
42150.5 6
< 0.1%
42602.2 5
< 0.1%
ValueCountFrequency (%)
104920490 8
< 0.1%
88541367.83 6
< 0.1%
68882988.5 6
< 0.1%
63922000.4 5
< 0.1%
58898796.71 7
< 0.1%
56486886.5 10
< 0.1%
55343559.67 12
< 0.1%
47753238.43 7
< 0.1%
46831017.29 7
< 0.1%
45298977.86 7
< 0.1%

channel_avg_n_comments_7D
Real number (ℝ)

Distinct5458
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4919.3035
Minimum0
Maximum391532.33
Zeros727
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:07.307889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile151
Q1732.4
median1754.7812
Q34073.8333
95-th percentile14956.767
Maximum391532.33
Range391532.33
Interquartile range (IQR)3341.4333

Descriptive statistics

Standard deviation16396.445
Coefficient of variation (CV)3.3330827
Kurtosis235.66761
Mean4919.3035
Median Absolute Deviation (MAD)1284.9351
Skewness13.268641
Sum1.6675455 × 108
Variance2.6884342 × 108
MonotonicityNot monotonic
2023-04-07T15:13:07.397696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 727
 
2.1%
7781.4 30
 
0.1%
295 28
 
0.1%
242 22
 
0.1%
3495.761905 21
 
0.1%
1459.952381 21
 
0.1%
2755.952381 21
 
0.1%
1733 21
 
0.1%
4430.5 20
 
0.1%
1181.259259 20
 
0.1%
Other values (5448) 32967
97.3%
ValueCountFrequency (%)
0 727
2.1%
1.2 5
 
< 0.1%
7.428571429 7
 
< 0.1%
10.16666667 6
 
< 0.1%
21.66666667 6
 
< 0.1%
24.85714286 7
 
< 0.1%
25.71428571 7
 
< 0.1%
35 5
 
< 0.1%
42.8 5
 
< 0.1%
43.66666667 3
 
< 0.1%
ValueCountFrequency (%)
391532.3333 6
< 0.1%
390366.75 8
< 0.1%
328344 7
< 0.1%
305362.3077 8
< 0.1%
262989.6429 8
< 0.1%
257948.3333 6
< 0.1%
240337.3333 1
 
< 0.1%
215094.8 5
< 0.1%
204925.3846 6
< 0.1%
203784.375 8
< 0.1%
Distinct5756
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite101
Infinite (%)0.3%
Meaninf
Minimum4.1385639
Maximuminf
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size529.7 KiB
2023-04-07T15:13:07.493955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.1385639
5-th percentile10.297411
Q116.877873
median24.756944
Q343.201361
95-th percentile117.70071
Maximuminf
Rangeinf
Interquartile range (IQR)26.323489

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)10.162117
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-04-07T15:13:07.578469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
inf 101
 
0.3%
8.547934337 30
 
0.1%
45.99068812 21
 
0.1%
84.94773089 21
 
0.1%
41.18057977 21
 
0.1%
73.64417762 20
 
0.1%
46.6807506 20
 
0.1%
199.9204591 19
 
0.1%
86.71208448 19
 
0.1%
24.59906717 18
 
0.1%
Other values (5746) 33608
99.1%
ValueCountFrequency (%)
4.138563933 5
< 0.1%
4.472997723 6
< 0.1%
4.740583372 4
< 0.1%
4.755976823 3
< 0.1%
5.131904613 1
 
< 0.1%
5.266603018 7
< 0.1%
5.437194522 6
< 0.1%
5.468607468 1
 
< 0.1%
5.556350062 5
< 0.1%
5.566456527 6
< 0.1%
ValueCountFrequency (%)
inf 101
0.3%
222729.8794 7
 
< 0.1%
20864.01656 6
 
< 0.1%
14370.46102 6
 
< 0.1%
2571.620098 3
 
< 0.1%
2380.256243 7
 
< 0.1%
1770.795299 8
 
< 0.1%
1576.339196 5
 
< 0.1%
1183.769964 7
 
< 0.1%
1053.161757 6
 
< 0.1%

Interactions

2023-04-07T15:13:04.730121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:58.735846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.223214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.952064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.681341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.277846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.985220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.882742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:58.899639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.383819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.115292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.842118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.434074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.152508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.956778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:58.975153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.459809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.195727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.924280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.508853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.235266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:05.035702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:59.056627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.540638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.276034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:02.006762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.591214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.318158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:05.196775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:59.224167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.715638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.442686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:02.176556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.757419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.491008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:05.285292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:59.302095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.795308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.520770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:02.256790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.830073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.569961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:05.367255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:12:59.386493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:00.881098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:01.607048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:02.345433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:03.913693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-07T15:13:04.654894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-07T15:13:05.469446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-07T15:13:05.633799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publishedAtview_countcategorycategory_trending_videos_last_7Dcategory_avg_n_views_7Dcategory_avg_n_comments_7Dcategory_view_like_ratio_7Dchannel_trending_videos_last_7Dchannel_avg_n_views_7Dchannel_avg_n_comments_7Dchannel_view_like_ratio_7D
02022-10-09 17:56:00+00:001089996Sports52.01.305889e+061444.36538537.4228989.01.126564e+062158.33333363.276702
12022-10-09 19:43:00+00:00546401Sports52.01.305889e+061444.36538537.42289813.04.583885e+05489.15384635.937076
22022-10-09 20:30:22+00:001013753Sports52.01.305889e+061444.36538537.4228989.01.126564e+062158.33333363.276702
32022-10-09 16:00:40+00:001838198Sports52.01.305889e+061444.36538537.4228987.02.402675e+062003.42857143.527955
42022-10-09 16:14:03+00:00503142Sports52.01.305889e+061444.36538537.4228989.01.126564e+062158.33333363.276702
52022-10-09 17:56:21+00:0057515Sports52.01.305889e+061444.36538537.4228985.06.531740e+04962.20000034.921621
62022-10-09 17:56:00+00:001333267Sports52.01.305889e+061444.36538537.4228989.01.126564e+062158.33333363.276702
72022-10-09 21:00:31+00:00734585Sports52.01.305889e+061444.36538537.4228986.08.471627e+05357.83333365.937318
82022-10-09 20:30:22+00:001315262Sports52.01.305889e+061444.36538537.4228989.01.126564e+062158.33333363.276702
92022-10-09 23:00:13+00:003528836Sports52.01.305889e+061444.36538537.4228986.04.140492e+062744.83333335.845885
publishedAtview_countcategorycategory_trending_videos_last_7Dcategory_avg_n_views_7Dcategory_avg_n_comments_7Dcategory_view_like_ratio_7Dchannel_trending_videos_last_7Dchannel_avg_n_views_7Dchannel_avg_n_comments_7Dchannel_view_like_ratio_7D
338882023-03-26 18:15:05+00:00863118Science & Technology43.01.473527e+067039.34883715.5941914.08.199628e+054349.25000013.778687
338892023-03-24 08:26:01+00:005744198Science & Technology57.02.917045e+068580.35087718.6882517.05.329434e+0631371.00000013.122355
338902023-03-24 17:00:01+00:002087856Science & Technology57.02.917045e+068580.35087718.6882516.01.503540e+064381.66666725.243485
338912023-03-25 07:16:58+00:00704267Science & Technology39.01.540560e+067315.25641015.7071715.06.642448e+051631.80000015.052820
338922023-03-29 20:14:44+00:00247372Science & Technology34.01.732413e+068922.23529415.0457761.02.473720e+05305.00000025.605217
338932023-03-27 20:49:43+00:001543932Science & Technology38.01.617551e+068364.52631615.3262563.01.404840e+069032.33333320.233030
338942023-03-27 17:07:16+00:00339363Science & Technology38.01.617551e+068364.52631615.3262563.02.788273e+05718.66666736.425797
338952023-03-26 18:15:05+00:00998388Science & Technology43.01.473527e+067039.34883715.5941914.08.199628e+054349.25000013.778687
338962023-03-24 08:26:01+00:005809755Science & Technology57.02.917045e+068580.35087718.6882517.05.329434e+0631371.00000013.122355
338972023-03-24 17:00:01+00:002200584Science & Technology57.02.917045e+068580.35087718.6882516.01.503540e+064381.66666725.243485